This disclosure relates generally to machine learning applications, and more particularly, to optimizing spending allocations in multi-channel digital marketing, via machine learning models.
In an online marketplace, certain businesses generate leads on potential customers for various services or products. In order to generate revenue, the lead generating business sells these leads to the business selling the service or product. For example, the lead generating business may be a mortgage broker who collects leads about potential customers looking for loans, and in turn, the broker sells the collected leads to a bank. The profit, generated by selling the lead, is the difference between the amount of money received from selling the lead and the amount money spent to generate the demand for the lead.
In order to generate demand for these leads, business may rely on digital marketing channels, such as search engine marketing (SEM), online advertising, and social media marketing (SMM). Each channel type (e.g. SEM or SMM) can have a hierarchical structure with multiple lower levels for elements, such as campaigns or ad groups. The decision about maximum spending can be made at the channel level or at any of the lower levels in the hierarchy. For example, at the root level, the business may decide to spend money on the entire search engine, or for specific key words at the lowest level. In another example, the business may decide to spend on groupings of keywords, based on campaigns in different geographical locations. Therefore, the term “channel” can signify either the actual channel at the root of the hierarchy or at any of the lower levels, alone or in combination. Further, a digital marketing channel, which contains a hierarchical structure within that channel, may be divided by account, account per state, account per product (e.g., the business may serve personal loans in one account and refinance loans in another account). Within each account, there may be different groups like an ad group, and within each group, the business may own certain keywords. For example, an account may contain 10 groups, and each group may be responsible for 1000 keywords.
An objective of the lead generating is to maximize the conversion rate (i.e., leads into revenue), and in turn, maximize the gross profit for a particular period of time. However, these businesses are faced with the problem of determining how many resources should be allocated to each channel on an hourly and daily basis. In other words, at any given time slot during the day, in which the time slot granularity depends on how often spending can be modified in the channels, the business is faced with a decision as to how much money should be invested in each of the digital marketing channels.
Further, the leads captured through those channels have different cost and quality levels and yield different conversion rates, which change depending on state (e.g., time of the day, day of the week, effects from business competition, economy patterns and seasonal events) and capacity for processing the leads. Capacity is determined by constraints that limit the speed at which leads can be converted into revenue, such as availability of inventory, personnel constraints or business partners' limitations. The capacity for processing leads in each time slot is a critical factor in determining spending so as to avoid generating leads that the business does not have resources to convert into revenue. If the businesses overproduces leads and exceeds the capacity allowed for that marketplace, then the business is overspending on leads that cannot be sold to the banks. If the business underproduces leads, then the business is not optimally utilizing the digital marketing channels.
Further, the demand for keywords may change based on various situations, for instance, multiple businesses or competitors may be bidding for the same keywords; the bidding can change per hour; and search behavior of potential customers may vary. Additionally, the bidding may fluctuate per day, state, or geographical region. Therefore, if the business bids on keywords using a static rule, the business will not be able to react effectively to changes in the market, and may overproduce or under produce leads.
Rules derived from domain expertise that try to capture relationships among the state variables to determine allocation often fail to produce optimal solutions given the non-linearity of those relationships, especially as the number of marketing channels grows.